Freeway Segment Speed Estimation Model based ed on Distribution Features of Floating Car Data

摘要

In order to gain well performance, typical speed estimation models based on GPS data requirehigh-sampling-rate GPS data. Due to insufficient sample amount, these models may become lesseffective for GPS points with low-sampling-rate. Therefore, in this study, it is aimed to fill this gapby developing effective speed estimation models for low-sampling-rate GPS data. The distributionfeatures of floating car data (FCD) on the target segment are analyzed, and thereby correspondingsegment speed estimation models based on these features are established. The distribution featuresof FCD on the target segment can generally be divided into three situations. Thus, this study putsforward a self-adaptive algorithm based on three speed estimation models including speed-timeintegral model, vehicle tracking model and speed-distance integral model to estimate the segmentspeed. A simulation experiment is conducted with the use of real OD data collected from Guangshen(GS) freeway in China, and the error range of the self-adaptive algorithm in different sample sizes ofFCD is analyzed.